




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["LongformerTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "LongformerForMaskedLM",
      "LongformerForMultipleChoice",
      "LongformerForQuestionAnswering",
      "LongformerForSequenceClassification",
      "LongformerForTokenClassification",
      "LongformerModel",
      "LongformerPreTrainedModel",
      "LongformerSelfAttention",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFLongformerForMaskedLM",
      "TFLongformerForMultipleChoice",
      "TFLongformerForQuestionAnswering",
      "TFLongformerForSequenceClassification",
      "TFLongformerForTokenClassification",
      "TFLongformerModel",
      "TFLongformerPreTrainedModel",
      "TFLongformerSelfAttention",
  ]


if TYPE_CHECKING:
  from .configuration_longformer import (
      LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      LongformerConfig,
      LongformerOnnxConfig,
  )
  from .tokenization_longformer import LongformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_longformer_fast import LongformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_longformer import (
        LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        LongformerForMaskedLM,
        LongformerForMultipleChoice,
        LongformerForQuestionAnswering,
        LongformerForSequenceClassification,
        LongformerForTokenClassification,
        LongformerModel,
        LongformerPreTrainedModel,
        LongformerSelfAttention,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_longformer import (
        TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLongformerForMaskedLM,
        TFLongformerForMultipleChoice,
        TFLongformerForQuestionAnswering,
        TFLongformerForSequenceClassification,
        TFLongformerForTokenClassification,
        TFLongformerModel,
        TFLongformerPreTrainedModel,
        TFLongformerSelfAttention,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''


import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import WhisperFeatureExtractor

if is_torch_available():
  import torch

UpperCAmelCase_					=   random.Random()


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[Any]					,       a_						:				Any=1.0					,       a_						:				Optional[Any]=None					,       a_						:				Union[str, Any]=None      ):
  if rng is None:
    __a								=       global_rng

  __a								=       []
  for batch_idx in range(shape[0]      ):
    values.append([]      )
    for _ in range(shape[1]      ):
      values[-1].append(rng.random() * scale      )

  return values





@require_torch
@require_torchaudio
class  __lowercase    (	unittest.TestCase			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase=7   ,				UpperCamelCase=400   ,				UpperCamelCase=2000   ,				UpperCamelCase=10   ,				UpperCamelCase=160   ,				UpperCamelCase=8   ,				UpperCamelCase=0.0   ,				UpperCamelCase=4000   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				)					->     Optional[Any]:
     __a								=       parent
     __a								=       batch_size
     __a								=       min_seq_length
     __a								=       max_seq_length
     __a								=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     __a								=       padding_value
     __a								=       sampling_rate
     __a								=       return_attention_mask
     __a								=       do_normalize
     __a								=       feature_size
     __a								=       chunk_length
     __a								=       hop_length







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     return {
         "feature_size": self.feature_size,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=False   ,				UpperCamelCase=False   )					->     int:
     def _flatten(UpperCamelCase   ):
       return list(itertools.chain(*UpperCamelCase   )   )

     if equal_length:
       __a								=       [floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
     else:
       # make sure that inputs increase in size
       __a								=       [
           floats_list((x, self.feature_size)   )
           for x in range(self.min_seq_length   ,				self.max_seq_length   ,				self.seq_length_diff   )
       ]
     if numpify:
       __a								=       [np.asarray(UpperCamelCase   ) for x in speech_inputs]
     return speech_inputs





@require_torch
@require_torchaudio
class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   WhisperFeatureExtractor if is_speech_available() else None







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       WhisperFeatureExtractionTester(self   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
       __a								=       feat_extract_first.save_pretrained(UpperCamelCase   )[0]
       check_json_file_has_correct_format(UpperCamelCase   )
       __a								=       self.feature_extraction_class.from_pretrained(UpperCamelCase   )

     __a								=       feat_extract_first.to_dict()
     __a								=       feat_extract_second.to_dict()
     __a								=       feat_extract_first.mel_filters
     __a								=       feat_extract_second.mel_filters
     self.assertTrue(np.allclose(UpperCamelCase   ,				UpperCamelCase   )   )
     self.assertEqual(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
       __a								=       os.path.join(UpperCamelCase   ,				'feat_extract.json'   )
       feat_extract_first.to_json_file(UpperCamelCase   )
       __a								=       self.feature_extraction_class.from_json_file(UpperCamelCase   )

     __a								=       feat_extract_first.to_dict()
     __a								=       feat_extract_second.to_dict()
     __a								=       feat_extract_first.mel_filters
     __a								=       feat_extract_second.mel_filters
     self.assertTrue(np.allclose(UpperCamelCase   ,				UpperCamelCase   )   )
     self.assertEqual(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     __a								=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     # create three inputs of length 800, 1000, and 1200
     __a								=       [floats_list((1, x)   )[0] for x in range(800   ,				1400   ,				200   )]
     __a								=       [np.asarray(UpperCamelCase   ) for speech_input in speech_inputs]

     # Test feature size
     __a								=       feature_extractor(UpperCamelCase   ,				padding='max_length'   ,				return_tensors='np'   ).input_features
     self.assertTrue(input_features.ndim == 3   )
     self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames   )
     self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size   )

     # Test not batched input
     __a								=       feature_extractor(speech_inputs[0]   ,				return_tensors='np'   ).input_features
     __a								=       feature_extractor(np_speech_inputs[0]   ,				return_tensors='np'   ).input_features
     self.assertTrue(np.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )   )

     # Test batched
     __a								=       feature_extractor(UpperCamelCase   ,				return_tensors='np'   ).input_features
     __a								=       feature_extractor(UpperCamelCase   ,				return_tensors='np'   ).input_features
     for enc_seq_a, enc_seq_a in zip(UpperCamelCase   ,				UpperCamelCase   ):
       self.assertTrue(np.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )   )

     # Test 2-D numpy arrays are batched.
     __a								=       [floats_list((1, x)   )[0] for x in (800, 800, 800)]
     __a								=       np.asarray(UpperCamelCase   )
     __a								=       feature_extractor(UpperCamelCase   ,				return_tensors='np'   ).input_features
     __a								=       feature_extractor(UpperCamelCase   ,				return_tensors='np'   ).input_features
     for enc_seq_a, enc_seq_a in zip(UpperCamelCase   ,				UpperCamelCase   ):
       self.assertTrue(np.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )   )

     # Test truncation required
     __a								=       [floats_list((1, x)   )[0] for x in range(200   ,				(feature_extractor.n_samples + 500)   ,				200   )]
     __a								=       [np.asarray(UpperCamelCase   ) for speech_input in speech_inputs]

     __a								=       [x[: feature_extractor.n_samples] for x in speech_inputs]
     __a								=       [np.asarray(UpperCamelCase   ) for speech_input in speech_inputs_truncated]

     __a								=       feature_extractor(UpperCamelCase   ,				return_tensors='np'   ).input_features
     __a								=       feature_extractor(UpperCamelCase   ,				return_tensors='np'   ).input_features
     for enc_seq_a, enc_seq_a in zip(UpperCamelCase   ,				UpperCamelCase   ):
       self.assertTrue(np.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )   )







   def 		UpperCamelCase__      (						self   )					->     int:
     import torch

     __a								=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __a								=       np.random.rand(100   ,				32   ).astype(np.floataa   )
     __a								=       np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
       __a								=       feature_extractor.pad([{'input_features': inputs}]   ,				return_tensors='np'   )
       self.assertTrue(np_processed.input_features.dtype == np.floataa   )
       __a								=       feature_extractor.pad([{'input_features': inputs}]   ,				return_tensors='pt'   )
       self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Tuple:
     __a								=       load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,				'clean'   ,				split='validation'   )
     # automatic decoding with librispeech
     __a								=       ds.sort('id'   ).select(range(UpperCamelCase   )   )[:num_samples]['audio']

     return [x["array"] for x in speech_samples]







   def 		UpperCamelCase__      (						self   )					->     int:
     # fmt: off
     __a								=       torch.tensor(
         [
             0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
             0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
             0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
             -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
         ]   )
     # fmt: on

     __a								=       self._load_datasamples(1   )
     __a								=       WhisperFeatureExtractor()
     __a								=       feature_extractor(UpperCamelCase   ,				return_tensors='pt'   ).input_features
     self.assertEqual(input_features.shape   ,				(1, 80, 3000)   )
     self.assertTrue(torch.allclose(input_features[0, 0, :30]   ,				UpperCamelCase   ,				atol=1e-4   )   )







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __a								=       self._load_datasamples(1   )[0]
     __a								=       ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
     __a								=       feat_extract.zero_mean_unit_var_norm([audio]   ,				attention_mask=UpperCamelCase   )[0]

     self.assertTrue(np.all(np.mean(UpperCamelCase   ) < 1e-3   )   )
     self.assertTrue(np.all(np.abs(np.var(UpperCamelCase   ) - 1   ) < 1e-3   )   )



